Blueprint · intermediate · 9 steps
Build a RAG Pipeline from Scratch
Build a document Q&A system with plain Python, Supabase pgvector, OpenAI embeddings, and Claude — no frameworks, just code you can read end to end.
All steps
01Step 1: What We're BuildingA document Q&A system that stores your files in Supabase, embeds them with pgvector, and answers questions using Claude — all with just one platform and two API calls.→02Step 2: Project SetupCreate the project directory, set up a Python virtual environment, install the four dependencies, and configure your API keys.→03Step 3: Set Up SupabaseCreate a Supabase project, enable the pgvector extension, and create the documents table that will store your text chunks and their embeddings.→04Step 4: Document LoaderLoad text files from a directory using plain Python and inspect the results — no frameworks needed.→05Step 5: Chunking and EmbeddingSplit documents into overlapping chunks with a simple Python function, then generate vector embeddings using OpenAI's API directly.→06Step 6: Store VectorsInsert your embedded chunks into the Supabase documents table and verify they are stored correctly.→07Step 7: Query EngineBuild the query engine that embeds a question, searches Supabase for relevant chunks, and generates an answer using Claude.→08Step 8: Test and IterateEvaluate your pipeline with real questions, tune chunk size and retrieval parameters, and improve answer quality systematically.→09Step 9: What's NextProduction considerations, extensions, and where to go from here with your RAG pipeline.→